Every Agent Memory comparison and buyer's guide for building AI agents — 14 pieces and counting. Each is a head-to-head or a “best X for Y” roundup with a sources-backed verdict.
Mem0, Letta, and Zep argue about how to structure an agent's memory. Redis's answer is quieter and more radical: make memory a server, and move the expensive part off your agent's request path.
The whole agent-memory leaderboard war — 84% vs 58% vs 75% — is being fought over a ten-conversation dataset called LOCOMO. Once you see how the numbers are made, you stop shopping on accuracy.
They get compared like rivals, but one is memory you program and the other is memory you call — and the benchmark leaderboard only measures one of them.
A Google PM's 'Always On Memory Agent' stores everything in SQLite and consolidates it with an LLM every 30 minutes. The 30-minute number tells you exactly what it's for — and what it isn't.
Claude's new consolidation loop replays an agent's day and writes down what it learned. The same mechanism that lifted one customer's task completion ~6x is the one that makes a poisoned lesson permanent.
TeleMem ships as a one-line replacement for Mem0 — import telemem as mem0 — and claims a 16-point accuracy edge. Read where that number comes from and you learn exactly which agent it's for.
Every serious agent-memory system is really a forgetting system. The hard part was never storing what the agent learns — it's pruning the contradictions and stale facts that quietly poison retrieval.
A June 2026 paper clocks three popular memory frameworks on the same benchmark: 118K, 632K, and 3.26M tokens per query. The 500x spread isn't noise — it's a design choice most teams never realize they're making.
The benchmarks that grade an agent's memory just moved the finish line from 9,000 tokens to 10 million — and the new one proves a million-token context window doesn't buy you long-term memory.
Mem0 says 92.5% on LoCoMo. Mastra says 95% on LongMemEval. Zep corrected its own 84% to 58%. They can't all be right — and the baseline that beats them all is the one no vendor charts.
Bigger context windows don't fix forgetting. The benchmarks that actually test agent memory — LoCoMo and LongMemEval — and what their question categories reveal about where it breaks.
"Stateless" is a misnomer. The state never disappears — it relocates to the client and gets replayed, in full, on every single turn. The real question is who stores it and who pays to replay it.
Most teams buy one vector store and call it 'memory.' It solves exactly one of the four problems — which is why the agent still loses the thread and repeats yesterday's mistake.
Three popular open-source memory frameworks that look like rivals but are actually three different bets on where memory lives — and how much of your architecture you hand over.
Not buyer's guides — the news, teardowns, and explainers behind this topic.
The memory libraries aren't competing on accuracy. They're competing on geography — where the remembering happens relative to your agent's loop. Pick the place, not the benchmark.
The hard problem of agent memory was never remembering. It's knowing when a remembered fact has quietly stopped being true.
Nine repositories tackling the hardest unsolved problem in agent design — remembering, retrieving, and forgetting across the lifetime of a conversation.
The industry has standardized how agents reach out to the world and ignored the harder question of what they keep — and that asymmetry is not an accident.